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On mining incomplete medical datasets: Ordering imputation and classification.

Chih-Wen Chen1, Wei-Chao Lin2, Shih-Wen Ke3

  • 1Department of Pharmacy, Kaohsiung Municipal Chinese Medical Hospital, Taiwan.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|September 28, 2015
PubMed
Summary

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This summary is machine-generated.

Instance selection effectively filters noisy data before missing value imputation in medical datasets. This pre-processing step improves classification accuracy when using support vector machine (SVM) classifiers.

Area of Science:

  • Data Science
  • Machine Learning
  • Medical Informatics

Background:

  • Incomplete medical datasets with missing values pose challenges for data mining.
  • Missing value imputation methods estimate missing data but depend heavily on observed data quality.

Purpose of the Study:

  • To evaluate the impact of instance selection on missing value imputation performance.
  • To compare four distinct processes combining instance selection and imputation for data classification.

Main Methods:

  • Experiments utilized 11 medical datasets with varying attribute types and missing data rates (10-50%).
  • Instance selection was performed using DROP3, and missing value imputation used k-nearest neighbor.
  • Support vector machine (SVM) classifiers assessed classification accuracy.
Keywords:
Instance selectionincomplete datamedical data miningmissing value imputation

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Main Results:

  • The process involving instance selection prior to imputation yielded superior performance for SVM classifiers.
  • This suggests that filtering outliers before imputation enhances the final classification outcome.

Conclusions:

  • Missing value imputation is crucial for incomplete medical datasets.
  • Instance selection can mitigate the negative impact of noisy data on imputation quality and classification performance.